SoroushVahidi/scidocs huggingface dataset:
Semantic assessment of shopping behavior using trajectories, shopping related actions, and context information
(2013)

The possibility of automatic understanding of customers’ shopping behavior and acting according to their needs is relevant in the marketing domain, attracting a lot of attention lately. In this work, we focus on the task of automatic assessment of customers’ shopping behavior, by proposing a multilevel framework. The framework is supported at low-level by different types of cameras, which are synchronized, facilitating efficient processing of information. A fish-eye camera is used for tracking, while a high-definition one serves for the action recognition task. The experiments are performed on both laboratory and real-life recordings in a supermarket. From the video recordings, we extract features related to the spatio-temporal behavior of trajectories, the dynamics and the time spent in each region of interest (ROI) in the shop and regarding the customer-products interaction patterns. Next ∗Corresponding Author. Phone: +31(0)15 2781537. Fax: +31(0)15 2787141. Email address: m.c.popa@tudelft.nl (M.C. Popa) Preprint submitted to Pattern Recognition Letters December 24, 2011 we analyze the shopping sequences using a Hidden Markov Model (HMM). We conclude that it is possible to accurately classify trajectories (93%), discriminate between different shopping related actions (91.6%), and recognize shopping behavioral types by means of the proposed reasoning model in 95% of the cases.
Generative networks as inverse problems with Scattering transforms
(2018)

Generative Adversarial Nets (GANs) and Variational Auto-Encoders (VAEs) provide impressive image generations from Gaussian white noise, but the underlying mathematics are not well understood. We compute deep convolutional network generators by inverting a fixed embedding operator. Therefore, they do not require to be optimized with a discriminator or an encoder. The embedding is Lipschitz continuous to deformations so that generators transform linear interpolations between input white noise vectors into deformations between output images. This embedding is computed with a wavelet Scattering transform. Numerical experiments demonstrate that the resulting Scattering generators have similar properties as GANs or VAEs, without learning a discriminative network or an encoder.
Graph Clustering Bandits for Recommendation
(2016)

We investigate an efficient context-dependent clustering technique for recommender systems based on exploration-exploitation strategies through multi-armed bandits over multiple users. Our algorithm dynamically groups users based on their observed behavioral similarity during a sequence of logged activities. In doing so, the algorithm reacts to the currently served user by shaping clusters around him/her but, at the same time, it explores the generation of clusters over users which are not currently engaged. We motivate the effectiveness of this clustering policy, and provide an extensive empirical analysis on real-world datasets, showing scalability and improved prediction performance over state-ofthe-art methods for sequential clustering of users in multi-armed bandit scenarios.
Entity Matching in Online Social Networks
(2013)

In recent years, Online Social Networks (OSNs) have essentially become an integral part of our daily lives. There are hundreds of OSNs, each with its own focus and offers for particular services and functionalities. To take advantage of the full range of services and functionalities that OSNs offer, users often create several accounts on various OSNs using the same or different personal information. Retrieving all available data about an individual from several OSNs and merging it into one profile can be useful for many purposes. In this paper, we present a method for solving the Entity Resolution (ER), problem for matching user profiles across multiple OSNs. Our algorithm is able to match two user profiles from two different OSNs based on machine learning techniques, which uses features extracted from each one of the user profiles. Using supervised learning techniques and extracted features, we constructed different classifiers, which were then trained and used to rank the probability that two user profiles from two different OSNs belong to the same individual. These classifiers utilized 27 features of mainly three types: name based features (i.e., the Soundex value of two names), general user info based features (i.e., the cosine similarity between two user profiles), and social network topological based features (i.e., the number of mutual friends between two users' friends list). This experimental study uses real-life data collected from two popular OSNs, Facebook and Xing. The proposed algorithm was evaluated and its classification performance measured by AUC was 0.982 in identifying user profiles across two OSNs.
Assessment of Motor Functioning in the Preschool Period
(2012)

The assessment of motor functioning in young children has become increasingly important in recent years with the acknowledgement that motor impairment is linked with cognitive, language, social and emotional difficulties. However, there is no one gold standard assessment tool to investigate motor ability in children. The aim of the current paper was to discuss the issues related to the assessment of motor ability in young pre-school children and to provide guidelines on the best approach for motor assessment. The paper discusses the maturational changes in brain development at the preschool level in relation to motor ability. Other issues include sex differences in motor ability at this young age, and evidence for this in relation to sociological versus biological influences. From the previous literature it is unclear what needs to be assessed in relation to motor functioning. Should the focus be underlying motor processes or movement skill assessment? Several key assessment tools are discussed that produce a general measure of motor performance followed by a description of tools that assess specific skills, such as fine and gross motor, ball and graphomotor skills. The paper concludes with recommendations on the best approach in assessing motor function in pre-school children.
The Analysis of the Mozart Effect on Visual Search
(2017)

Over twenty-five years ago, researchers began to study the effects of music on the brain and cognition. The Mozart Effect is a term that was first coined by Dr. Alfred Tomatis in 1991 in his book titled “Porquoi Mozart” in which Tomatis advocated the use of Mozart music as alternative medicine for those suffering from dyslexia, autism, and other learning disorders. [Sorensen 2008] This effect, originally called “The Tomatis Effect” was observed by Tomatis during therapy sessions with his patients. From his observations, Tomatis claimed that Mozart music helped promote healing and could even cure depression. The definition of the term evolved over time; in the present day, the term “Mozart Effect” is typically used to refer to the phenomena of improved spatial reasoning after exposure to Mozart music.
A two-dimensional model for the study of interpersonal attraction.
(2014)

We describe a model for understanding interpersonal attraction in which attraction can be understood as a product of the initial evaluations we make about others. The model posits that targets are evaluated on two basic dimensions, capacity and willingness, such that affective and behavioral attraction result from evaluations of (a) a target's capacity to facilitate the perceiver's goals/needs and (b) a target's potential willingness to facilitate those goals/needs. The plausibility of the two-dimensional model of attraction is evaluated vis-à-vis the extant literature on various attraction phenomena including the reciprocity of liking effect, pratfall effect, matching hypothesis, arousal effects, and similarity effect. We conclude that considerable evidence across a wide range of phenomena supports the idea that interpersonal attraction is principally determined by inferences about the target's capacity and willingness.
Designing mobile interfaces for novice and low-literacy users
(2011)

While mobile phones have found broad application in bringing health, financial, and other services to the developing world, usability remains a major hurdle for novice and low-literacy populations. In this article, we take two steps to evaluate and improve the usability of mobile interfaces for such users. First, we offer an ethnographic study of the usability barriers facing 90 low-literacy subjects in India, Kenya, the Philippines, and South Africa. Then, via two studies involving over 70 subjects in India, we quantitatively compare the usability of different points in the mobile design space. In addition to text interfaces such as electronic forms, SMS, and USSD, we consider three text-free interfaces: a spoken dialog system, a graphical interface, and a live operator.
 Our results confirm that textual interfaces are unusable by first-time low-literacy users, and error prone for literate but novice users. In the context of healthcare, we find that a live operator is up to ten times more accurate than text-based interfaces, and can also be cost effective in countries such as India. In the context of mobile banking, we find that task completion is highest with a graphical interface, but those who understand the spoken dialog system can use it more quickly due to their comfort and familiarity with speech. We synthesize our findings into a set of design recommendations.
Proxemic Transitions: Designing Shape-Changing Furniture for Informal Meetings
(2017)

The field of Shape-Changing Interfaces explores the qualities of physically dynamic artifacts. At furniture-scale, such artifacts have the potential of changing the ways we collaborate and engage with interiors and physical spaces. Informed by theories of proxemics, empirical studies of informal meetings and design work with shape-changing furniture, we develop the notion of proxemic transitions. We present three design aspects of proxemic transitions: transition speed, stepwise reconfiguration, and radical shifts. The design aspects focus on how to balance between physical and digital transformations in designing for proxemic transitions. Our contribution is three-fold: 1) the notion of proxemic transitions, 2) three design aspects to consider in designing for proxemic transitions, and 3) initial exploration of how these design aspects might generate designs of dynamic furniture. These contributions outline important aspects to consider when designing shape-changing furniture for informal workplace meetings.
Analysis Operator Learning and its Application to Image Reconstruction
(2013)

Exploiting a priori known structural information lies at the core of many image reconstruction methods that can be stated as inverse problems. The synthesis model, which assumes that images can be decomposed into a linear combination of very few atoms of some dictionary, is now a well established tool for the design of image reconstruction algorithms. An interesting alternative is the analysis model, where the signal is multiplied by an analysis operator and the outcome is assumed to be sparse. This approach has only recently gained increasing interest. The quality of reconstruction methods based on an analysis model severely depends on the right choice of the suitable operator. In this paper, we present an algorithm for learning an analysis operator from training images. Our method is based on lp-norm minimization on the set of full rank matrices with normalized columns. We carefully introduce the employed conjugate gradient method on manifolds, and explain the underlying geometry of the constraints. Moreover, we compare our approach to state-of-the-art methods for image denoising, inpainting, and single image super-resolution. Our numerical results show competitive performance of our general approach in all presented applications compared to the specialized state-of-the-art techniques.
Deep, complex, invertible networks for inversion of transmission effects in multimode optical fibres
(2018)

We use complex-weighted, deep networks to invert the effects of multimode optical fibre distortion of a coherent input image. We generated experimental data based on collections of optical fibre responses to greyscale input images generated with coherent light, by measuring only image amplitude (not amplitude and phase as is typical) at the output of 1m and 10m long, 105 μm diameter multimode fibre. This data is made available as the Optical fibre inverse problem Benchmark collection. The experimental data is used to train complex-weighted models with a range of regularisation approaches. A unitary regularisation approach for complexweighted networks is proposed which performs well in robustly inverting the fibre transmission matrix, which is compatible with the physical theory. A benefit of the unitary constraint is that it allows us to learn a forward unitary model and analytically invert it to solve the inverse problem. We demonstrate this approach, and outline how it has the potential to improve performance by incorporating knowledge of the phase shift induced by the spatial light modulator.
SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing
(2017)

With the recent advance of wearable devices and Internet of Things (IoTs), it becomes attractive to implement the Deep Convolutional Neural Networks (DCNNs) in embedded and portable systems. Currently, executing the software-based DCNNs requires high-performance servers, restricting the widespread deployment on embedded and mobile IoT devices. To overcome this obstacle, considerable research efforts have been made to develop highly-parallel and specialized DCNN accelerators using GPGPUs, FPGAs or ASICs.
 Stochastic Computing (SC), which uses a bit-stream to represent a number within [-1, 1] by counting the number of ones in the bit-stream, has high potential for implementing DCNNs with high scalability and ultra-low hardware footprint. Since multiplications and additions can be calculated using AND gates and multiplexers in SC, significant reductions in power (energy) and hardware footprint can be achieved compared to the conventional binary arithmetic implementations. The tremendous savings in power (energy) and hardware resources allow immense design space for enhancing scalability and robustness for hardware DCNNs.
 This paper presents SC-DCNN, the first comprehensive design and optimization framework of SC-based DCNNs, using a bottom-up approach. We first present the designs of function blocks that perform the basic operations in DCNN, including inner product, pooling, and activation function. Then we propose four designs of feature extraction blocks, which are in charge of extracting features from input feature maps, by connecting different basic function blocks with joint optimization. Moreover, the efficient weight storage methods are proposed to reduce the area and power (energy) consumption. Putting all together, with feature extraction blocks carefully selected, SC-DCNN is holistically optimized to minimize area and power (energy) consumption while maintaining high network accuracy. Experimental results demonstrate that the LeNet5 implemented in SC-DCNN consumes only 17 mm2 area and 1.53 W power, achieves throughput of 781250 images/s, area efficiency of 45946 images/s/mm2, and energy efficiency of 510734 images/J.
Look who's talking: speech style and social context in language input to infants are linked to concurrent and future speech development.
(2014)

Language input is necessary for language learning, yet little is known about whether, in natural environments, the speech style and social context of language input to children impacts language development. In the present study we investigated the relationship between language input and language development, examining both the style of parental speech, comparing 'parentese' speech to standard speech, and the social context in which speech is directed to children, comparing one-on-one (1:1) to group social interactions. Importantly, the language input variables were assessed at home using digital first-person perspective recordings of the infants' auditory environment as they went about their daily lives (N =26, 11- and 14-months-old). We measured language development using (a) concurrent speech utterances, and (b) word production at 24 months. Parentese speech in 1:1 contexts is positively correlated with both concurrent speech and later word production. Mediation analyses further show that the effect of parentese speech-1:1 on infants' later language is mediated by concurrent speech. Our results suggest that both the social context and the style of speech in language addressed to children are strongly linked to a child's future language development.
The Internet of Things: a security point of view
(2016)

ed as devices to provide services at low-levels as network discovery services, metadata exchange services, and asynchronous publish and subscribe event in (Kranenburg et al. 2011; Sundmaeker et al. 2010); In (Peris-Lopez et al. 2006), a representational state transfer (REST) is defined to increase interoperability between Page 14 of 32 http://mc.manuscriptcentral.com/intr Internet Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 or Peer Rview loosely coupled services and distributed applications. In (Hernandez-Castro et al. 2013), the services layer introduced a service provisioning process (SPP) that can provide the interaction between applications and services. It is important to design an effective security strategy to protect services against attacks in the service layer. The security requirements in the service layer include: • Authorization, service authentication, group authentication, privacy protection, integrity, integrity, security of keys, non-repudiation, anti-replay, availability,
Chest pathology detection using deep learning with non-medical training
(2015)

In this work, we examine the strength of deep learning approaches for pathology detection in chest radiographs. Convolutional neural networks (CNN) deep architecture classification approaches have gained popularity due to their ability to learn mid and high level image representations. We explore the ability of CNN learned from a non-medical dataset to identify different types of pathologies in chest x-rays. We tested our algorithm on a 433 image dataset. The best performance was achieved using CNN and GIST features. We obtained an area under curve (AUC) of 0.87-0.94 for the different pathologies. The results demonstrate the feasibility of detecting pathology in chest x-rays using deep learning approaches based on non-medical learning. This is a first-of-its-kind experiment that shows that Deep learning with ImageNet, a large scale non-medical image database may be a good substitute to domain specific representations, which are yet to be available, for general medical image recognition tasks.
Crowdsourcing of Pollution Data using Smartphones
(2010)

In this paper we present our research into participatory sensing based solutions for the collection of data on urban pollution and nuisance. In the past 2 years we have been involved in the NoiseTube project which explores a crowdsourcing approach to measuring and mapping urban noise pollution using smartphones. By involving the general public and using off-the-shelf smartphones as noise sensors, we seek to provide a low cost solution for citizens to measure their personal exposure to noise in their everyday environment and participate in the creation of collective noise maps by sharing their geo-localized and annotated measurements with the community. We believe our work represents an interesting example of the novel mobile crowdsourcing applications which are enabled by ubiquitous computing systems. Furthermore we believe the NoiseTube system, and the currently ongoing validation experiments, provide an illustrative context for some of the open challenges faced by creators of ubiquitous crowdsourcing applications and services in general. We will also take the opportunity to present the insights we gained into some of the challenges.
Architecture, the Backbone of Robotic Systems
(2000)

Architectures form the backbone of complete robotic systems. The right choice of architecture can go a long way in facilitating the specification, implementation and validation of robotic systems. Conversely, of course, the wrong choice can make one's life miserable. We present some of the needs of robotic systems, describe some general classes of robot architectures, and discuss how different architectural styles can help in addressing those needs. The paper, like the field itself, is somewhat preliminary, yet it is hoped that it will provide guidance for those who use, or develop, robot architectures.
CLASSIFICATION OF DEFECTS IN SOFTWARE USING DECISION TREE ALGORITHM
(2013)

Software defects due to coding errors continue to plague the industry with disastrous impact, especially in the enterprise application software category. Identifying how much of these defects are specifically due to coding errors is a challenging problem. Defect prevention is the most vivid but usually neglected aspect of software quality assurance in any project. If functional at all stages of software development, it can condense the time, overheads and wherewithal entailed to engineer a high quality product. In order to reduce the time and cost, we will focus on finding the total number of defects if the test case shows that the software process not executing properly. That has occurred in the software development process. The proposed system classifying various defects using decision tree based defect classification technique, which is used to group the defects after identification. The classification can be done by employing algorithms such as ID3 or C4.5 etc. After the classification the defect patterns will be measured by employing pattern mining technique. Finally the quality will be assured by using various quality metrics such as defect density, etc. The proposed system will be implemented in JAVA.
SympGraph: a framework for mining clinical notes through symptom relation graphs
(2012)

As an integral part of Electronic Health Records (EHRs), clinical notes pose special challenges for analyzing EHRs due to their unstructured nature. In this paper, we present a general mining framework SympGraph for modeling and analyzing symptom relationships in clinical notes.
 A SympGraph has symptoms as nodes and co-occurrence relations between symptoms as edges, and can be constructed automatically through extracting symptoms over sequences of clinical notes for a large number of patients. We present an important clinical application of SympGraph: symptom expansion, which can expand a given set of symptoms to other related symptoms by analyzing the underlying SympGraph structure. We further propose a matrix update algorithm which provides a significant computational saving for dynamic updates to the graph. Comprehensive evaluation on 1 million longitudinal clinical notes over 13K patients shows that static symptom expansion can successfully expand a set of known symptoms to a disease with high agreement rate with physician input (average precision 0.46), a 31% improvement over baseline co-occurrence based methods. The experimental results also show that the expanded symptoms can serve as useful features for improving AUC measure for disease diagnosis prediction, thus confirming the potential clinical value of our work.